summaryrefslogtreecommitdiff
path: root/numpy/ma/extras.py
diff options
context:
space:
mode:
Diffstat (limited to 'numpy/ma/extras.py')
-rw-r--r--numpy/ma/extras.py259
1 files changed, 137 insertions, 122 deletions
diff --git a/numpy/ma/extras.py b/numpy/ma/extras.py
index 3be4d3625..2e3b84e1c 100644
--- a/numpy/ma/extras.py
+++ b/numpy/ma/extras.py
@@ -81,15 +81,14 @@ def count_masked(arr, axis=None):
>>> a[1, 2] = ma.masked
>>> a[2, 1] = ma.masked
>>> a
- masked_array(data =
- [[0 1 2]
- [-- 4 --]
- [6 -- 8]],
- mask =
- [[False False False]
- [ True False True]
- [False True False]],
- fill_value=999999)
+ masked_array(
+ data=[[0, 1, 2],
+ [--, 4, --],
+ [6, --, 8]],
+ mask=[[False, False, False],
+ [ True, False, True],
+ [False, True, False]],
+ fill_value=999999)
>>> ma.count_masked(a)
3
@@ -132,15 +131,15 @@ def masked_all(shape, dtype=float):
--------
>>> import numpy.ma as ma
>>> ma.masked_all((3, 3))
- masked_array(data =
- [[-- -- --]
- [-- -- --]
- [-- -- --]],
- mask =
- [[ True True True]
- [ True True True]
- [ True True True]],
- fill_value=1e+20)
+ masked_array(
+ data=[[--, --, --],
+ [--, --, --],
+ [--, --, --]],
+ mask=[[ True, True, True],
+ [ True, True, True],
+ [ True, True, True]],
+ fill_value=1e+20,
+ dtype=float64)
The `dtype` parameter defines the underlying data type.
@@ -188,16 +187,16 @@ def masked_all_like(arr):
>>> import numpy.ma as ma
>>> arr = np.zeros((2, 3), dtype=np.float32)
>>> arr
- array([[ 0., 0., 0.],
- [ 0., 0., 0.]], dtype=float32)
+ array([[0., 0., 0.],
+ [0., 0., 0.]], dtype=float32)
>>> ma.masked_all_like(arr)
- masked_array(data =
- [[-- -- --]
- [-- -- --]],
- mask =
- [[ True True True]
- [ True True True]],
- fill_value=1e+20)
+ masked_array(
+ data=[[--, --, --],
+ [--, --, --]],
+ mask=[[ True, True, True],
+ [ True, True, True]],
+ fill_value=1e+20,
+ dtype=float32)
The dtype of the masked array matches the dtype of `arr`.
@@ -492,28 +491,45 @@ if apply_over_axes.__doc__ is not None:
Examples
--------
- >>> a = ma.arange(24).reshape(2,3,4)
- >>> a[:,0,1] = ma.masked
- >>> a[:,1,:] = ma.masked
- >>> print(a)
- [[[0 -- 2 3]
- [-- -- -- --]
- [8 9 10 11]]
-
- [[12 -- 14 15]
- [-- -- -- --]
- [20 21 22 23]]]
- >>> print(ma.apply_over_axes(ma.sum, a, [0,2]))
- [[[46]
- [--]
- [124]]]
+ >>> a = np.ma.arange(24).reshape(2,3,4)
+ >>> a[:,0,1] = np.ma.masked
+ >>> a[:,1,:] = np.ma.masked
+ >>> a
+ masked_array(
+ data=[[[0, --, 2, 3],
+ [--, --, --, --],
+ [8, 9, 10, 11]],
+ [[12, --, 14, 15],
+ [--, --, --, --],
+ [20, 21, 22, 23]]],
+ mask=[[[False, True, False, False],
+ [ True, True, True, True],
+ [False, False, False, False]],
+ [[False, True, False, False],
+ [ True, True, True, True],
+ [False, False, False, False]]],
+ fill_value=999999)
+ >>> np.ma.apply_over_axes(np.ma.sum, a, [0,2])
+ masked_array(
+ data=[[[46],
+ [--],
+ [124]]],
+ mask=[[[False],
+ [ True],
+ [False]]],
+ fill_value=999999)
Tuple axis arguments to ufuncs are equivalent:
- >>> print(ma.sum(a, axis=(0,2)).reshape((1,-1,1)))
- [[[46]
- [--]
- [124]]]
+ >>> np.ma.sum(a, axis=(0,2)).reshape((1,-1,1))
+ masked_array(
+ data=[[[46],
+ [--],
+ [124]]],
+ mask=[[[False],
+ [ True],
+ [False]]],
+ fill_value=999999)
"""
@@ -558,14 +574,19 @@ def average(a, axis=None, weights=None, returned=False):
1.25
>>> x = np.ma.arange(6.).reshape(3, 2)
- >>> print(x)
- [[ 0. 1.]
- [ 2. 3.]
- [ 4. 5.]]
+ >>> x
+ masked_array(
+ data=[[0., 1.],
+ [2., 3.],
+ [4., 5.]],
+ mask=False,
+ fill_value=1e+20)
>>> avg, sumweights = np.ma.average(x, axis=0, weights=[1, 2, 3],
... returned=True)
- >>> print(avg)
- [2.66666666667 3.66666666667]
+ >>> avg
+ masked_array(data=[2.6666666666666665, 3.6666666666666665],
+ mask=[False, False],
+ fill_value=1e+20)
"""
a = asarray(a)
@@ -676,9 +697,9 @@ def median(a, axis=None, out=None, overwrite_input=False, keepdims=False):
>>> np.ma.median(x)
2.5
>>> np.ma.median(x, axis=-1, overwrite_input=True)
- masked_array(data = [ 2. 5.],
- mask = False,
- fill_value = 1e+20)
+ masked_array(data=[2.0, 5.0],
+ mask=[False, False],
+ fill_value=1e+20)
"""
if not hasattr(a, 'mask'):
@@ -856,15 +877,14 @@ def compress_rowcols(x, axis=None):
... [1, 0, 0],
... [0, 0, 0]])
>>> x
- masked_array(data =
- [[-- 1 2]
- [-- 4 5]
- [6 7 8]],
- mask =
- [[ True False False]
- [ True False False]
- [False False False]],
- fill_value = 999999)
+ masked_array(
+ data=[[--, 1, 2],
+ [--, 4, 5],
+ [6, 7, 8]],
+ mask=[[ True, False, False],
+ [ True, False, False],
+ [False, False, False]],
+ fill_value=999999)
>>> np.ma.compress_rowcols(x)
array([[7, 8]])
@@ -937,25 +957,24 @@ def mask_rows(a, axis=None):
[0, 0, 0]])
>>> a = ma.masked_equal(a, 1)
>>> a
- masked_array(data =
- [[0 0 0]
- [0 -- 0]
- [0 0 0]],
- mask =
- [[False False False]
- [False True False]
- [False False False]],
- fill_value=999999)
+ masked_array(
+ data=[[0, 0, 0],
+ [0, --, 0],
+ [0, 0, 0]],
+ mask=[[False, False, False],
+ [False, True, False],
+ [False, False, False]],
+ fill_value=1)
+
>>> ma.mask_rows(a)
- masked_array(data =
- [[0 0 0]
- [-- -- --]
- [0 0 0]],
- mask =
- [[False False False]
- [ True True True]
- [False False False]],
- fill_value=999999)
+ masked_array(
+ data=[[0, 0, 0],
+ [--, --, --],
+ [0, 0, 0]],
+ mask=[[False, False, False],
+ [ True, True, True],
+ [False, False, False]],
+ fill_value=1)
"""
return mask_rowcols(a, 0)
@@ -982,25 +1001,23 @@ def mask_cols(a, axis=None):
[0, 0, 0]])
>>> a = ma.masked_equal(a, 1)
>>> a
- masked_array(data =
- [[0 0 0]
- [0 -- 0]
- [0 0 0]],
- mask =
- [[False False False]
- [False True False]
- [False False False]],
- fill_value=999999)
+ masked_array(
+ data=[[0, 0, 0],
+ [0, --, 0],
+ [0, 0, 0]],
+ mask=[[False, False, False],
+ [False, True, False],
+ [False, False, False]],
+ fill_value=1)
>>> ma.mask_cols(a)
- masked_array(data =
- [[0 -- 0]
- [0 -- 0]
- [0 -- 0]],
- mask =
- [[False True False]
- [False True False]
- [False True False]],
- fill_value=999999)
+ masked_array(
+ data=[[0, --, 0],
+ [0, --, 0],
+ [0, --, 0]],
+ mask=[[False, True, False],
+ [False, True, False],
+ [False, True, False]],
+ fill_value=1)
"""
return mask_rowcols(a, 1)
@@ -1078,12 +1095,12 @@ def intersect1d(ar1, ar2, assume_unique=False):
Examples
--------
- >>> x = array([1, 3, 3, 3], mask=[0, 0, 0, 1])
- >>> y = array([3, 1, 1, 1], mask=[0, 0, 0, 1])
- >>> intersect1d(x, y)
- masked_array(data = [1 3 --],
- mask = [False False True],
- fill_value = 999999)
+ >>> x = np.ma.array([1, 3, 3, 3], mask=[0, 0, 0, 1])
+ >>> y = np.ma.array([3, 1, 1, 1], mask=[0, 0, 0, 1])
+ >>> np.ma.intersect1d(x, y)
+ masked_array(data=[1, 3, --],
+ mask=[False, False, True],
+ fill_value=999999)
"""
if assume_unique:
@@ -1216,9 +1233,9 @@ def setdiff1d(ar1, ar2, assume_unique=False):
--------
>>> x = np.ma.array([1, 2, 3, 4], mask=[0, 1, 0, 1])
>>> np.ma.setdiff1d(x, [1, 2])
- masked_array(data = [3 --],
- mask = [False True],
- fill_value = 999999)
+ masked_array(data=[3, --],
+ mask=[False, True],
+ fill_value=999999)
"""
if assume_unique:
@@ -1483,7 +1500,9 @@ class mr_class(MAxisConcatenator):
Examples
--------
>>> np.ma.mr_[np.ma.array([1,2,3]), 0, 0, np.ma.array([4,5,6])]
- array([1, 2, 3, 0, 0, 4, 5, 6])
+ masked_array(data=[1, 2, 3, ..., 4, 5, 6],
+ mask=False,
+ fill_value=999999)
"""
def __init__(self):
@@ -1524,19 +1543,19 @@ def flatnotmasked_edges(a):
Examples
--------
>>> a = np.ma.arange(10)
- >>> flatnotmasked_edges(a)
- [0,-1]
+ >>> np.ma.flatnotmasked_edges(a)
+ array([0, 9])
>>> mask = (a < 3) | (a > 8) | (a == 5)
>>> a[mask] = np.ma.masked
>>> np.array(a[~a.mask])
array([3, 4, 6, 7, 8])
- >>> flatnotmasked_edges(a)
+ >>> np.ma.flatnotmasked_edges(a)
array([3, 8])
>>> a[:] = np.ma.masked
- >>> print(flatnotmasked_edges(ma))
+ >>> print(np.ma.flatnotmasked_edges(a))
None
"""
@@ -1588,7 +1607,7 @@ def notmasked_edges(a, axis=None):
>>> np.array(am[~am.mask])
array([0, 1, 2, 3, 6])
- >>> np.ma.notmasked_edges(ma)
+ >>> np.ma.notmasked_edges(am)
array([0, 6])
"""
@@ -1709,15 +1728,11 @@ def notmasked_contiguous(a, axis=None):
[slice(0, 1, None), slice(2, 4, None), slice(7, 9, None), slice(11, 12, None)]
>>> np.ma.notmasked_contiguous(ma, axis=0)
- [[slice(0, 1, None), slice(2, 3, None)], # column broken into two segments
- [], # fully masked column
- [slice(0, 1, None)],
- [slice(0, 3, None)]]
+ [[slice(0, 1, None), slice(2, 3, None)], [], [slice(0, 1, None)], [slice(0, 3, None)]]
>>> np.ma.notmasked_contiguous(ma, axis=1)
- [[slice(0, 1, None), slice(2, 4, None)], # row broken into two segments
- [slice(3, 4, None)],
- [slice(0, 1, None), slice(3, 4, None)]]
+ [[slice(0, 1, None), slice(2, 4, None)], [slice(3, 4, None)], [slice(0, 1, None), slice(3, 4, None)]]
+
"""
a = asarray(a)
nd = a.ndim
View Lorry Controller Status